Predictive Analytics Help Automakers Reduce the Need for Hardware Recalls

Imagine a fleet of twenty trucks rolling down some vast, endless highway. Some go faster, some slower, others falter as they pass through differing weather conditions. As they inch their way through hundreds of thousands of miles, the trucks start giving out. Tires are worn out at uneven rates. Finally, there is just one vehicle left, whose tires seem to have outlasted all the others’.

This particular truck incorporated predictive analytics. Each truck drove the way it thought best, but only one used data analysis to understand what that truly meant. As trucking companies try to pass the half-million-mile mark for tires, automakers are struggling to understand how they can extend the lifespan of every piece of hardware on a vehicle. This can be achieved by analyzing every single thing that happens to every single car on the road and then using this data to update hardware, perform preventative maintenance and avoid expensive recalls or repairs. It is not just about avoiding blowouts, it is also about transforming the relationship between the car and the road.

How Can Data Analytics Reduce Recalls?

In 2015, the US auto industry saw a record number of recalled vehicles – 51.2 million – spread across 868 separate recalls. In 2016, the number increased to 53.2 million. This does not mean that cars are somehow getting worse, this has more to do with increased vigilance and better knowledge of faulty systems –similar to finding that there has been a record number of people diagnosed with a disease, rather than it indicating an epidemic, it often means that we are in a better position to detect and cure the illness.

Suppose a car performed really well, with an excellent safety record, except for one thing – when there was a little bit of rain, the braking software read it as a lot of rain and acted accordingly, changing the speed and causing the car to stop more suddenly that it should. Over time, this would impact the brakes, creating a potentially dangerous situation, but this would be virtually impossible to detect ahead of time — unless, of course, the automaker could analyze billions of pieces of data to discover that vehicles in areas with light rainfall were performing differently — a possibility now thanks to big data analytics.

Once the problem is found, getting to a solution does not take long. At this point, there could even be a couple of options. The automaker could arrange a recall to fix the impacted hardware, or they could send targeted Over-The-Air (OTA) software updates to the cars on the road in order to adjust the rain-sensing algorithm. It depends on the situation, of course, but they might be able to avoid a recall altogether.

The Power of Prediction

Predictive analytics goes further than just diagnosing the problem. Perhaps its most important function is the ability to enable OEMs to send out software updates to compensate for specific road conditions. Different drivers have different driving styles and lifestyles, and the hardware that powers their cars will be impacted accordingly. Someone driving in the damp Pacific Northwest mountains has a different experience than someone driving in the stop-and-go flatness of Chicago traffic. Understanding what every car is experiencing can help OEMs design future models and keep current vehicles on the road longer by performing individual adjustments and preventive maintenance.

Other benefits of understanding this information include:

Early identification of hazards: It would be better to diagnose potential problems before they impact anyone at all. By relying on comprehensive data visualization techniques, OEMs may be able to discern that, say, certain cars will have braking issues before the first faulty stop.

Supply chain management: Predictive analytics can provide a bird’s view of which parts might be needed when. Parts will still wear out eventually, of course, but automakers will be able to understand when a spate of breakdowns is likely to occur in China, for instance, due to conditions and time of purchase, well in advance, allowing them enough time to have an optimum supply of the required parts. The global supply chain should be more proactive than reactive. Being able to anticipate the need for updates, especially recalls, can help OEMs manage them in a timely manner.

Improved customer satisfaction: It does not matter if a customer knows that a recall will make them safer. They first think of the inconvenience that it caused them and the fact that they were potentially in danger. Avoiding recalls will increase customer confidence and help protect OEMs’ reputations.

Reputations can also be protected with OTA software updates, which can improve vehicle performance without the drivers needing to come into the dealership with their vehicles. Using adaptive delta compression, software updates can make safe and efficient changes to a car’s performance, further reducing the need for recalls.

Predictive analytics make it possible to prevent bad situations due to faulty hardware. OTA technology makes the transmission of software updates that protect that hardware quick and painless. We will never have a tire that lasts forever but by understanding everything that might happen to that tire, in every condition, rolling down every mile of the highway, we can ensure that the tires and every other piece of hardware that powers a car last longer than before.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.